# Copyright 2024 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import pytest
import numpy as np

import mindspore.context as context
import mindspore as ms
from mindspore import Tensor
from mindspore import ops
import tests.st.utils.test_utils as test_utils
from tests.mark_utils import arg_mark


@test_utils.run_with_cell
def forward_func(x, indices):
    return ops.max_unpool3d(x, indices, kernel_size=2, stride=1, padding=0)


@test_utils.run_with_cell
def backward_func(x, indices):
    return ms.grad(forward_func, (0))(x, indices)


@arg_mark(plat_marks=['platform_ascend'], level_mark='level1', card_mark='onecard', essential_mark='unessential')
@pytest.mark.parametrize("context_mode", [context.GRAPH_MODE, context.PYNATIVE_MODE])
def test_maxunpool3d_float32_normal(context_mode):
    """
    Feature: maxunpool3d
    Description: test maxunpool3d
    Expectation: expect correct result.
    """
    context.set_context(mode=context_mode, device_target="Ascend")
    x = Tensor(np.array([[[[[0, 1], [8, 9]]]]]).astype(np.float32))
    indices = Tensor(np.array([[[[[0, 1], [2, 3]]]]]).astype(np.int64))
    output = forward_func(x, indices)
    expected = np.array([[[[[0., 1., 8.],
                            [9., 0., 0.],
                            [0., 0., 0.]],
                           [[0., 0., 0.],
                            [0., 0., 0.],
                            [0., 0., 0.]]]]], np.float32)
    np.testing.assert_allclose(output.asnumpy(), expected, rtol=1e-3)

    x_grad = backward_func(x, indices)
    assert x_grad.asnumpy().shape == x.shape
